Comparison of four immunoassays to measure serum ferritin concentrations and iron deficiency prevalence among non-pregnant Cambodian women and Congolese children
Bibliographic record
Abstract
BACKGROUND: Global standardization of ferritin assays is lacking, which could have direct implications on the accurate measurement and comparability of ferritin concentration and iron deficiency (ID) prevalence rates in at-risk populations. METHODS: We measured serum ferritin concentrations using four immunoassays: the s-ELISA and the AxSYM™ analyzer were compared among 420 non-pregnant Cambodian women; the Centaur® XP analyzer, s-ELISA, and AxSYM™ analyzer were compared among a subset of 100 Cambodian women; and the s-ELISA and the Elecsys® 2010 analyzer were compared among 226 Congolese children aged 6-59 months. RESULTS: Median ferritin concentrations (adjusted for inflammation) ranged between 48 and 91 μg/L among Cambodian women and between 54 and 55 μg/L among Congolese children. ID prevalence ranged from 2% to 10% among Cambodian women and 5% to 7% among Congolese children. Bias between methods varied widely (-9 to 45 μg/L) among women, and was 43 μg/L among children. Bias was lower when ferritin values outside of the s-ELISA measurement range (>250 μg/L) were excluded. CONCLUSIONS: The observed differences in ferritin concentrations likely reflect different ferritin isoforms, antibodies, and calibrators used across assays and by different laboratories. However, despite differences in ferritin concentrations, ID prevalence was relatively similar and low across all methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".